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Miniaturized pressure sensors fabricated via micro-electro-mechanical systems (MEMSs) technology are ubiquitous in modern applications. However, the massively produced MEMS pressure sensors, prior to being practically used, need to be calibrated one by one to eliminate or minimize nonlinearity and zero drift. This paper presents a systematic design for the testing and calibration process of MEMS-based absolute pressure sensors. Firstly, a numerical analysis is carried out using finite element method (FEM) simulation, which verifies the accuracy of the temperature control of the physical calibration system. The simulation results reveal a slight non-uniformity of temperature distribution, which is then taken into consideration in the calibration algorithm. Secondly, deploying a home-made calibration system, the MEMS pressure sensors are tested automatically and rapidly. The experimental results show that each batch, which consists of nine sensors, can be calibrated in 80 min. The linearity and temperature coefficient (TC) of the pressure sensors are reduced from 46.5% full-scale (FS) and −1.35 × 10−4 V·K−1 to 1.5% FS and −8.8 × 10−7 V·K−1.

Micro direct methanol fuel cells (μDMFCs) are a promising power source for microelectronic devices and systems. As the operating state and performance of a μDMFC is generally determined by both electrochemical polarization and methanol crossover, it is important to monitor the methanol concentration in μDMFCs. Here, we design and fabricate a microwave sensor and integrate it with a μDMFC for the online detection of methanol concentration in a nonintrusive way. The sensing area is set at the bottom of the anode chamber of a μDMFC which exhibits a maximum output power density of 28.8 mW cm−2 at 30 °C. With a square ring structure, the dual-mode microwave sensor shows a sensitivity of 9.5 MHz mol−1 L. Furthermore, the importance of methanol concentration monitoring is demonstrated in the long term. A relatively smooth methanol decline curve was obtained, which indicated a normal and stable operating status of the μDMFC. Derived from real-time recording data, fuel utilization was additionally calculated as 28.5%.

Mobile edge computing is the network technology for providing computing resources of edge computing server to Internet of Things (IoT) applications. Additionally, wireless power transfer (WPT) technology can provide stable energy supplying to IIoT nodes and to overcome the limited node lifetime problem faced when using batteries. In this paper, the wireless powered mobile edge computing network is considered where the edge computing server transfers RF energy to IIoT nodes which use harvested energy to offload partial computation workload based on OFDMA and also conduct local computation. The aim is to maximise the weighted sum computation rate by jointly optimising the WPT duration and the amount of energy used for offloading at each node for each time frame. This paper proposes an offloading approach based on deep reinforcement learning which is able to quickly obtain the near‐optimal offloading solutions. Specifically, the original offloading problem is decomposed into the sub‐problem of optimising the energy allocated for offloading under a given WPT duration and the top‐problem of optimising the WPT duration. Simulation results demonstrate that the proposed algorithm can achieve the near‐optimal weighted sum computation rate with very low complexity, which is tailored for the practical dynamic‐channel environment.

Age of Information (AoI) is a newly introduced metric that quantifies the freshness and timeliness of data, playing a crucial role in applications reliant on time-sensitive information. Minimizing AoI through optimal scheduling is challenging, especially in energy-constrained Internet of Things (IoT) networks. In this work, we begin by analyzing a simplified cognitive radio network (CRN) where a single secondary user (SU) harvests RF energy from the primary user and transmits status update packets when the PU spectrum is available. Time is divided into equal time slots, and the SU performs either energy harvesting, spectrum sensing, or status update transmission in each slot. To optimize the AoI within the CRN, we formulate the sequential decision-making process as a partially observable Markov decision process (POMDP) and employ dynamic programming to determine optimal actions. Then, we extend our investigation to evaluate the long-term average weighted sum of AoIs for a multi-SU CRN. Unlike the single-SU scenario, decisions must be made regarding which SU performs sensing and which SU forwards the status update packs. Given the partially observable nature of the PU spectrum, we propose an enhanced Deep Q-Network (DQN) algorithm. Simulation results demonstrate that the proposed policies significantly outperform the myopic policy. Additionally, we analyze the effect of various parameter settings on system performance.

This paper investigates the secrecy communication in an underlay cognitive radio (CR) networks with one primary user (PU) as well as multiple PUs, where the radio frequency (RF) energy-harvesting secondary user (SU) transmits the confidential information to the destination in the presence of a potential eavesdropper. We introduce a RF energy-harvesting secondary jammer (SJ) to secure the SU transmissions. The system works in time slots, where each time slot is divided into the energy transfer (ET) phase and the information transfer (IT) phase. In ET phase, the SU and SJ capture energy from the PU transmissions; in the IT phase, the SU uses the harvested energy to transmit information to the destination without causing the harmful interference to the PU transmissions, while the SJ utilizes the captured energy to generate jamming signals to the eavesdropper to secure the SU transmissions. We aim to maximize the secrecy rate for SU transmissionsby jointly optimizing the time allocation between ET phase and IT phase and the transmit power allocation at the SU and SJ. We first formulate the secrecy rate maximization as non-convex optimization problems. Then, we propose efficient nested form algorithms for the non-convex problems. In the outer layer, we obtain the optimal time allocation by the one dimension search method. In the inner layer, we obtain the optimal transmit power allocation by the DC programming, where the Lagrange duality method is employed to solve the convex approximation problem. Simulation results verify that the proposed schemes essentially improve the secrecy rate of the secondary network as compared to the benchmark schemes.

Both arbuscular mycorrhizal (AM) fungi and root hairs play important roles in plant uptake of water and mineral nutrients. To reveal the relative importance of mycorrhiza and root hairs in plant water relations, a bald root barley ( brb ) mutant and its wild type ( wt ) were grown with or without inoculation of the AM fungus Rhizophagus intraradices under well-watered or drought conditions, and plant physiological traits relevant to drought stress resistance were recorded. The experimental results indicated that the AM fungus could almost compensate for the absence of root hairs under drought-stressed conditions. Moreover, phosphorus (P) concentration, leaf water potential, photosynthetic rate, transpiration rate, stomatal conductance, and water use efficiency were significantly increased by R. intraradices but not by root hairs, except for shoot P concentration and photosynthetic rate under the drought condition. Root hairs even significantly decreased root P concentration under drought stresses. These results confirm that AM fungi can enhance plant drought tolerance by improvement of P uptake and plant water relations, which subsequently promote plant photosynthetic performance and growth, while root hairs presumably contribute to the improvement of plant growth and photosynthetic capacity through an increase in shoot P concentration.

This paper presents a numerical and experimental study on the performance of a methanol steam reformer integrated with a hydrogen/air combustion reactor for hydrogen production. A CFD-based 3D model with mass and momentum transport and temperature characteristics is established. The simulation results show that better performance is achieved in the cross-U type reactor compared to either a tubular reactor or a parallel-U type reactor because of more effective heat transfer characteristics. Furthermore, Cu-based micro reformers of both cross-U and parallel-U type reactors are designed, fabricated and tested for experimental validation. Under the same condition for reforming and combustion, the results demonstrate that higher methanol conversion is achievable in cross-U type reactor. However, it is also found in cross-U type reactor that methanol reforming selectivity is the lowest due to the decreased water gas shift reaction under high temperature, thereby carbon monoxide concentration is increased. Furthermore, the reformed gas generated from the reactors is fed into a high temperature proton exchange membrane fuel cell (PEMFC). In the test of discharging for 4 h, the fuel cell fed by cross-U type reactor exhibits the most stable performance.

This study investigates the challenge of ensuring timely information delivery in wireless powered sensor networks (WPSNs), where multiple sensors forward status-update packets to a base station (BS). Time is partitioned to multiple time blocks, with each time block dedicated to either data packet transmission or energy transfer. Our objective is to minimize the long-term average weighted sum of the Age of Information (WAoI) for physical processes monitored by sensors. We formulate this optimization problem as a multi-stage stochastic optimization program. To tackle this intricate problem, we propose a novel approach that leverages Lyapunov optimization to transform the complex original problem into a sequence of per-time-bock deterministic problems. These deterministic problems are then solved using model-free deep reinforcement learning (DRL). Simulation results demonstrate that our proposed algorithm achieves significantly lower WAoI compared to the DQN, AoI-based greedy, and energy-based greedy algorithms. Furthermore, our method effectively mitigates the issue of excessive instantaneous AoI experienced by individual sensors compared to the DQN.

This paper presents a membrane electrode assembly (MEA) with a double-catalytic layered structure to improve the performance of the micro direct methanol fuel cell. The inner and outer parts of the double-catalytic layer comprise an unsupported and carbon-supported catalyst, respectively. A two-dimensional two-phase model of mass transport and electrochemical reaction is established and simulated to analyze the superiority of the double-catalytic layered structure. Simulation results show that this structure has a more uniform current density distribution and less over-potential across the catalyst layer. Methanol crossover is also reduced. Experimental results confirm that the MEA with the double-catalytic layered structure exhibits better performance than the traditional MEA. The adoption of a gas diffusion electrode as the outer catalytic layer and a catalyst-coated membrane as the inner layer of the double-catalytic layered structure can further improve the performance of the MEA. Both simulation and experimental results show the existence of an optimum number of metal loadings of the inner and outer parts of the double-catalytic layer.

In a photovoltaic power enhancer system, when it is operated in current-control mode, significant nonuniform temperature distribution occurs in the converter due to thermal coupling effects, dissipative boundary conditions, and differences in device losses within the in-phase bridge. Accurate on-site estimation of the power device’s junction temperature is critical in the system design. To address this problem, a novel thermal behavior estimation model based on electro-thermal analysis is proposed in this paper, which can be used for asymmetric power MOSFETs in a photovoltaic power enhancer system. Thermal coupling effects and dissipative boundary conditions are, firstly, analyzed in a three-dimensional finite element model. A coupling impedance matrix is constructed through step power response extraction to describe the significant thermal coupling effects among devices. The complete heat sink is decoupled into several sub-parts representing different dissipative boundary conditions. A compact RC network model for estimating junction temperature is established based on the combination of the coupling impedance and the sub-heat-sink impedance. The proposed model is verified by finite element simulation and experimental measurement.